Image-based Computational Biology

In this research group, led by Dr. Joost Beltman, the aim is to employ mathematical and computational dynamical modelling approaches in order to quantitatively and mechanistically understand the dynamical behaviour and regulation of intracellular networks of genes, proteins and metabolites as well as of that of cellular interactions in health and disease.

Obtaining a quantitative understanding of cellular dynamics at various levels as well as describing and predicting how drugs influence these dynamics represent important steps for the discovery of novel concepts in drug development. To achieve this, quantitative data acquired at multiple time points need to be integrated into dynamical models. A powerful way to generate the required quantitative measurements is to perform time-lapse imaging of cells in which specific proteins are followed within single cells because they are labelled fluorescently.

Depending on the question asked, one can use such data to focus on dynamic changes over time within single cells which potentially lead to cellular fate changes such as death, proliferation or differentiation, to dynamically follow cell migration at short or long term for understanding of cellular localization and associated biological function, or to quantify population level outcome of single-cell fate decisions. Therefore, we frequently (but not exclusively) exploit such dynamic imaging data by incorporating them into dynamical models. These data are either generated by external collaborators, or in-house by local collaborators.

The mathematical and computational models that we apply to experimental data are constructed at different biological scales. In some cases we focus at the level of individual cells and their interactions and in other cases at the intracellular level and molecular interactions. When appropriate for the question addressed, we also combine these into multi-level models. The types of models we use include primarily classical Ordinary Differential Equation (ODE) models, stochastic models, agent-based models and spatial models such as the ‘cellular Potts model’. We apply these models to two application areas. First, we focus on approaches to quantitatively describe various aspects of tumor growth and metastasis and therapy-induced manipulation of these processes.

One of our interests here includes the role of the immune system in tumor control and destruction. Second, we aim to contribute to the prediction of drug safety by achieve a mechanistic understanding of the multitude of cellular stress pathways that are involved in protecting cells from chemical perturbations. In summary, we aim for a highly interdisciplinary approach in which our models make biologically meaningful predictions that can subsequently be tested experimentally. For example, with our models we are able to quantify which processes are most important in determining therapeutic outcome, thus giving direction to the processes that need to be optimized for the therapies of the future.